6533b7d5fe1ef96bd1263eb6
RESEARCH PRODUCT
Simulated Annealing Technique for Fast Learning of SOM Networks
Antonino FiannacaAlfonso UrsoGiuseppe Di FattaRiccardo RizzoSalvatore Gagliosubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniComputer Science::Machine LearningArtificial IntelligenceSOM Simulated annealing Clustering Fast learningArtificial neural networkWake-sleep algorithmbusiness.industryComputer scienceTopology (electrical circuits)computer.software_genreAdaptive simulated annealingGeneralization errorData visualizationComputingMethodologies_PATTERNRECOGNITIONArtificial IntelligenceSimulated annealingUnsupervised learningData miningbusinessCluster analysisSelf Organizing map simulated annealingcomputerSoftwaredescription
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-12-28 |